Soft money : private foundations of party politics

In the area of campaign financing in federal elections, one of the most controversial issues is that of soft money. Soft money refers to those funds raised by the national party organizations for use on various grassroots and party-building activities. but which are not subject to the restraints of federal campaign finance law. Critics contend that these party-building activitie, such as generic television advertising, voter registration and get-out-the vote drives, provide ancillary benefits to federal candidates and should, therefore, be subject to federal contribution and expenditure limits. Critics further argue that because these funds are not subject to federal law and do benefit federal candidates, the national parties raise monies in amounts and from sources, such as corporations and unions, that are prohibited under federal law. Efforts to gain a better understanding of soft money have been hampered by a lack of data, as the national parties were not required to disclose their soft money receipts and transactions until 1991. The purpose of this study is to analyze data recently made available in an attempt to add the import of empirical evidence to the debate over soft money. The nature, size and timing of soft money contributions are investigated and national party soft money disbursements are examined. The findings suggest that any attempts to reform the soft money system must first consider its compensatory benefits. Most prominently, this includes the extent to which soft money has promoted the resurgence of the national party organizations in the context of election politics.

Machine learning and small robot navigation

Developing successful navigation and mapping strategies is an essential part of autonomous robot research. However, hardware limitations often make for inaccurate systems. This project serves to investigate efficient alternatives to mapping an environment, by first creating a mobile robot, and then applying machine learning to the robot and controlling systems to increase the robustness of the robot system. My mapping system consists of a semi-autonomous robot drone in communication with a stationary Linux computer system. There are learning systems running on both the robot and the more powerful Linux system. The first stage of this project was devoted to designing and building an inexpensive robot. Utilizing my prior experience from independent studies in robotics, I designed a small mobile robot that was well suited for simple navigation and mapping research. When the major components of the robot base were designed, I began to implement my design. This involved physically constructing the base of the robot, as well as researching and acquiring components such as sensors. Implementing the more complex sensors became a time-consuming task, involving much research and assistance from a variety of sources. A concurrent stage of the project involved researching and experimenting with different types of machine learning systems. I finally settled on using neural networks as the machine learning system to incorporate into my project. Neural nets can be thought of as a structure of interconnected nodes, through which information filters. The type of neural net that I chose to use is a type that requires a known set of data that serves to train the net to produce the desired output. Neural nets are particularly well suited for use with robotic systems as they can handle cases that lie at the extreme edges of the training set, such as may be produced by "noisy" sensor data. Through experimenting with available neural net code, I became familiar with the code and its function, and modified it to be more generic and reusable for multiple applications of neural nets.